An online learning program which can tell when a student is becoming bored and inattentive is one of the key developments forecast to reshape university education in Australia in the next five years, according to a new report. The 2016 NMC Technology Outlook for Australian Tertiary Education says that so-called "affective computing", which is able to use video imagery of facial expressions to discern human emotions, will soon be coupled with online learning platforms to encourage students to keep their minds on their work. The report says this is likely to be adopted by universities in the next four to five years. It forecast "online learning situations wherein a computerised tutor reacts to facial cues of boredom in a student in an effort to motivate or boost their confidence." "Software technology will literally learn to learn, interpreting and responding to learners' most nuanced gestures and emotions – whether they are feeling bored, intimidated or satisfied," says Brenda Frisk, head of learning technology at Open Universities Australia, a partner in the report.

Learning needs to be less like memorization, and more like…Angry Birds. Half of school dropouts name boredom as the number one reason they left. The post is about why the future of education will be about flipping our current model on its head and about how key exponential technologies like AI, VR and gamification are going to drive a revolution in education. In the traditional education system, you start at an "A," and every time you get something wrong, your score gets lower and lower. You start with zero, and every time you come up with something right, your score gets higher and higher. It completely flips the way we currently learn, and it's addictively fun.

Scientists have developed a new artificial intelligence software that can turn any smartphone into an eye-tracking device. Eye-tracking technology - which can determine where in a visual scene people are directing their gaze - has been widely used in psychological experiments and marketing research, but the required pricey hardware has kept it from finding consumer applications. In addition to making existing applications of eye-tracking technology more accessible, the system developed by researchers at Massachusetts Institute of Technology (MIT) and University of Georgia may enable new computer interfaces or help detect signs of incipient neurological disease or mental illness. "Since few people have the external devices, there is no big incentive to develop applications for them," said Aditya Khosla, an MIT graduate student. "Since there are no applications, there's no incentive for people to buy the devices. We thought we should break this circle and try to make an eye tracker that works on a single mobile device, using just your front-facing camera," he said.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

The TensorFlow project is bigger than you might realize. The fact that it's a library for deep learning, and its connection to Google, has helped TensorFlow attract a lot of attention. Cool stuff, but--especially for someone hoping to explore machine learning for the first time--TensorFlow can be a lot to take in. Let's break it down so we can see and understand every moving part. We'll explore the data flow graph that defines the computations your data will undergo, how to train models with gradient descent using TensorFlow, and how TensorBoard can visualize your TensorFlow work. The examples here won't solve industrial machine learning problems, but they'll help you understand the components underlying everything built with TensorFlow, including whatever you build next!

This free evening talk will explore the fields of Deep Learning and Computer Vision, using lie detection from video as an example. Nick studied Computer science at Imperial College before moving to the industry to work as a Data Scientist. He is active in the London startup scene and is interested in the role technology plays in our emotional wellbeing. He is an advocate of designing for happiness. If you are interested in progressing further with Machine Learning and Data Learning, learn more at the Data Science Bootcamp in Python.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Learning needs to be less like memorization, and more like… Angry Birds. Half of school dropouts name boredom as the No. 1 reason they left. The blog is about why the future of education will be about flipping our current model on its head and about how key exponential technologies like AI, VR and gamification are going to drive a revolution in education. In the traditional education system, you start at an "A." And every time you get something wrong, your score gets lower and lower.

Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, we'll build and evaluate predictive models from real-world text using scikit-learn. Subscribe to the Data School newsletter: http://www.dataschool.io/subscribe/ OTHER RESOURCES My scikit-learn video series: https://www.youtube.com/playlist?list... My pandas video series: https://www.youtube.com/playlist?list... JOIN THE DATA SCHOOL COMMUNITY Blog: http://www.dataschool.io

Organizations create style guides to capture the rationale of their design decisions and help other teams build great experiences. You might have read gov.UK's service manual or the U.S. Digital Services Playbook. I wanted to do the same for chatbots build on the Facebook's messenger platform. At Sure, we are creating an online assistant that helps you find food and drinks that are better for yourself and the planet. It is still very early days for bots, so I wanted to take the opportunity to share some of our early learnings.